Heyvaert, Carl-ErikStouthuysen, KristofVerdonck, Tim2024-05-232024-05-232024http://hdl.handle.net/20.500.12127/7465This paper introduces a structural topic model (STM) to assess the disclosure materiality of risk types detailed in risk factor disclosures (RFDs). Amid concerns about the generic nature of current RFD practices, the SEC's recent amendments push for disclosures that solely convey material, firm-specific risks. Our study leverages a unique dataset covering the entire RFD mandate from 2006 to 2023, enabling a longitudinal analysis that tests the amendments' effectiveness in enhancing the provision of firm-specific information among disclosed risks through the lens of stock price synchronicity. By employing a novel machine learning methodology to quantify the disclosed risk types and assessing their individual impact on stock price movements, our approach offers a more nuanced understanding of how disclosures influence investor behavior. Our findings challenge the efficacy of recent regulatory changes, suggesting that not all types of risk disclosures have adapted to meet the heightened standards of materiality. By addressing methodological limitations in previous studies, our research contributes to the automated textual analysis in financial reporting and offers a comprehensive view on the evolving effectiveness of RFDs. This study not only enriches the academic literature on risk disclosure materiality but also provides empirical evidence that could guide future regulatory adjustments by the SEC.enRisk Factor DisclosureStructural Topic ModelMaterialityStock Price Informativeness286358119751